title	colorFrom	colorTo	sdk	pinned
School of Statistics	blue	indigo	static	false

SOS - School of Statistics

Interactive visualizations for exploring statistical and machine learning concepts. Each page runs entirely in the browser (HTML, CSS, JavaScript with Chart.js) without requiring a server or build step.

Available Pages

Classification

• Direct Classification: Generate synthetic 2D datasets and observe how class separation affects Gaussian Naive Bayes classifier performance. Displays ROC curve, AUC, confusion matrix, and standard metrics (accuracy, precision, recall, specificity, F1-score).

• Inverse Classification: Directly set confusion matrix values (TP, FP, TN, FN) and observe resulting metrics, ROC curve, and simulated score distributions. Parameters can be locked to constrain totals.

Regression

• Linear Regression: Interactive point placement on canvas with linear or polynomial regression fitting. Displays residuals, coefficient of determination (R²), and regression diagnostics. Supports zoom, point dragging, and confidence band display.

Signal Processing

• Fourier Transform: Compose signals from sine waves and visualize their frequency spectrum. Up to 4 components with frequency, amplitude, and phase control. Displays time-domain signal, magnitude spectrum, phase spectrum, and signal metrics (sampling rate, Nyquist frequency, frequency resolution, total power, RMS).

Project Structure

.
├── direct_classifier.html          # Direct classification (Naive Bayes)
├── inverse_classifier.html         # Inverse classification (confusion matrix)
├── linear_regression.html          # Linear/polynomial regression
├── fourier_transform.html          # Fourier transform
├── CHANGELOG.md                    # Version history
├── LICENSE
├── README.md
└── src/
    ├── assets/
    │   └── logo.jpg
    ├── css/
    │   ├── style.css               # Shared base styles
    │   ├── direct_classifier.css   # Page-specific styles
    │   ├── embedding_distances.css
    │   ├── fourier_transform.css
    │   └── linear_regression.css
    └── js/
        ├── common.js               # Shared utilities (metrics, ROC, matrices, drag, etc.)
        ├── direct_classifier.js
        ├── fourier_transform.js
        ├── inverse_classifier.js
        ├── linear_regression.js
        └── logistic_regression.js

Usage

1. Clone the repository.
2. Open any .html file in your web browser.

No dependencies to install — all libraries are loaded via CDN.

Versioning

See CHANGELOG.md for release history.

Roadmap

Upcoming

• Bias-Variance Tradeoff Explorer: visualize bias-variance decomposition with polynomial fitting of increasing degree
• k-Nearest Neighbors Playground: interactive point placement and k-NN decision boundary visualization
• Gradient Descent Visualizer: real-time navigation on 2D loss surfaces, optimizer comparison
• Principal Component Analysis (PCA) Step-by-Step: Gaussian cloud generation and principal component visualization
• Clustering Algorithms Visualizer: k-Means and DBSCAN comparison on various dataset shapes
• Neural Network Architecture & Forward Pass Visualizer: layer-by-layer fully-connected network construction
• Tokenization & Embedding Visualizer: tokenization and 2D embedding space projection
• Attention Mechanism Visualizer: Transformer attention mechanism visualization
• Probability Distributions Explorer: exploration of standard distributions (Normal, Uniform, Exponential, Poisson, Binomial, Beta, Gamma, Chi-squared)
• Markov Chain Text Generator: Markov chain construction and text generation
• A/B Testing Calculator: statistical tool for hypothesis testing
• Voice Signal Waveform Analyzer: audio recording, waveform display, spectrogram computation, and dominant frequency identification

License

See the LICENSE file.